Poker is one of the most complicated games humans have ever played. It requires complex strategy, intuition and reasoning based on hidden information. This makes it difficult for AIs to play and win against human opponents.
To conquer poker, AIs first have to understand how to defeat humans. Then they need to adjust their strategies to account for different players’ strengths and weaknesses.
In a recent game of Texas Hold’em, an artificial intelligence system called Pluribus defeated top professional poker players in a six-player tournament. This is the first time an AI has successfully matched and outplayed a human player in a competitive poker game.
Ultimately, Pluribus’s success depends on its ability to learn from its mistakes. It uses machine learning to continually look back at past plays and determine if its strategy would have been better under different circumstances. If it does, it will then use that information to improve its game.
It also needs to be able to read a player’s hand and know what their betting patterns are. These factors can help an AI decide if it should fold or call.
Another challenge is how to predict the probability of winning a hand. This is especially important in a game like poker where the players don’t have the full set of cards and can’t see each other’s hands. In order to do this, AIs must rely on a number of imperfect techniques such as the Bayes theorem, Nash equilibrium, Monte Carlo simulation or neural networks.
These methods are all imperfect, but they are still useful for testing AIs. And they’re also easier to implement than some of the more advanced algorithms such as deep learning that use large data sets and a vast number of computer processors.
For example, a system that uses deep learning can be taught to make decisions by playing the game trillions of times and then analyzing each hand in an attempt to find the best strategy. This allows the system to get a good understanding of the game and how to beat humans.
While this approach is great for two-player games, it isn’t so effective in multi-player games because the number of players and their potential moves are much greater. So Brown and Sandholm radically overhauled Libratus’s search algorithm to deal with multiple players.
They found a way to search for the best move by using a limited-lookahead search algorithm instead of searching all the way to the end of a game, which would be computationally expensive. This method is a standard way of handling perfect-information games such as chess and Go, but it’s much more challenging in imperfect-information games such as poker.
This is a crucial step toward AI being able to successfully beat humans in any game with imperfect information. It could potentially open up new fields for AI research such as automated negotiations, drug development and security, as well as self-driving cars and fraud detection.
This is a huge accomplishment for the field of AI, which is finally making progress in solving a variety of complex problems. The next big challenge is to move on to games with more complex hidden information, such as chess and video games.